# Documentation of lin_remove_NaN_defunct

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## Function Synopsis

`y = lin_remove_NaN(x,xtim,show)`

## Help text

```lin_remove_NaN:  linearly remove a time series from data

Y = lin_remove(Xdat, Xtim) removes the best linear fit of Xtim to
each column of Xdat.  If Xdat is N-dimensional, then it is
assumed that the time series Xtim will be removed from the first
dimension of Xdat.

Y = lin_remove(Xdat) assumes Xtim is evenly spaced, so the linear
trend is removed.

```

## Cross-Reference Information

This function calls

## Listing of function lin_remove_NaN_defunct

```function y = lin_remove_NaN(x,xtim,show)

sz = size(x); ndim = length(sz);
if (ndim == 2) & (sz(1) == 1); x = x(:); end;
sz = size(x); ndim = length(sz);

if nargin < 2; xtim = [1:sz(1)]/sz(1); end;

if nargin < 3; show = 0; end;

if (size(xtim, 1))==1; xtim=xtim(:); end;

if size(xtim, 1)~=sz(1);
error('Xtim must have the same length as the first dimension of Xdat');
end

%  Reshape x if necessary, assuming the dimension to be
%  detrended is the first

if ndim > 2;
x = reshape(x, sz(1), prod(sz(2:ndim)));
end

%  Remove means from data and time series
N = size(x, 1);
xtim = xtim - ones(N, 1)*mean2(xtim);
x = x - ones(N, 1)*mean2(x);

%  Remove Regression

[N, m] = size(x);
[NN, mm] = size(xtim);

y = repmat(NaN, [N m]);

for i = 1:m;
if show;
disp(['Iteration:  ' num2str(i)]);
end
kp = find(~isnan(x(:,i)));
for j = 1:mm
kp = intersect(kp, find(~isnan(xtim(:, j))));
end
y(kp,i) = x(kp,i) - xtim(kp,:)*(xtim(kp,:)\x(kp,i));
end

%  Reshape output so it is the same dimension as input

if ndim > 2;
y = reshape(y, sz);
end

```